[1-11C]Acetate PET

C-11 labelled acetate

11C-labelled acetate, [1-11C]acetate (Pike et al., 1982), is used to assess perfusion, oxidative metabolism, and fatty acid synthesis. After intravenous administration of [1-11C]acetate, the build-up phase of radioactivity in the tissue is related (but not linearly) to tissue perfusion. Washout of radiolabel represents the formation of [11C]CO2 in tissue, and is therefore related to oxygen consumption (Buck et al., 1991; Klein et al., 2001). Slower washout at later time points may represent the rate of fatty acid synthesis (Lewis et al., 2014).

Monocarboxylate transporters and possibly sodium-dependent monocarboxylate transporter and aquaporins transport acetate into cells. Mitochondrial acetyl-CoA synthetase (acetate-CoA ligase) contributes to the uptake of [1-11C]acetate in the myocardium; in hypoxic tumours cytoplasmic/nuclear acetyl-CoA synthetase is upregulated (Yoshii et al., 2009). In the brain acetate is predominantly metabolized by astrocytes.

The pancreas, bowel, kidneys, and spleen receive the highest radiation doses, but no urinary excretion can be detected (Seltzer et al., 2004). Dosimetry of [1-11C]acetate is favourable, because the main metabolite, [11C]CO2, is rapidly exhaled.

Model input function

For quantitative analysis, arterial sampling should be performed, because concentrations of [11C]acetate and its label-carrying metabolites differ between arterial and venous blood. Input function can often be derived from dynamic image with reasonable accuracy, especially in cardiac studies, but also if the arch of aorta, abdominal aorta, or iliac arteries are located in the dynamic PET image. Only BTAC can be derived from the image, and separate blood samples are required for conversion of BTAC to PTAC and for metabolite correction; alternatively, population-based corrections can be applied.

Delay and dispersion

Time delay and dispersion between the blood sampling site and tissue of interest could be estimated and corrected with different methods.

Richard et al (2019) compared the maximum initial slope of the blood curve from the aortic arch, from where the blood curve derived, to the curve from a large blood vessel near the target region. Delay-corrected curves were then used to fit and correct the dispersion in accordance with monoexponential formula (Iida et al., 1986).

Blood versus plasma

In a dog study, [1-14C]acetate concentration in red blood cells was negligible at least for 2 hours (Persson et al., 1991), although suitable transporters are present at least human in RBCs. Plasma-to-blood ratio in a [1-11C]acetate mice study (Authier et al., 2008) was ∼1.45 at 5 min p.i., and the ratio then decreased to 1.35 at 15 min, 1.32 at 30 min, and 1.18 at 60 min, suggesting that unchanged [1-11C]acetate stays in plasma, but radioactive metabolites can penetrate the red blood cell membrane. Physiologically, acetate concentration in the human blood is higher than in plasma (Tollinger et al., 1979).

Radioactive metabolites

The main metabolite of [1-11C]acetate in blood is [11C]CO2 (Buck et al., 1991). Despite of the fact that [11C]CO2 is rapidly exhaled, it is present in the blood, and need to be accounted for in full quantitative analysis. In humans, 10 min after administration ∼60% of the blood activity is due to [11C]CO2, and less than 20% due to [1-11C]acetate (Buck et al., 1991; Sun et al, 1998). The metabolite fractions plotted as a function of time clearly show a sigmoidal pattern (Buck et al., 1991, Fig 5; Sun et al., 1997, Fig 4; Sun et al, 1998, Table 3). Buck et al. (1991) did not fit the measured fractions nor directly used those in the data analysis, but instead the metabolite correction was included in the compartmental model for myocardium. The metabolite fractions were modelled using exponential function

, and when fitted together with the myocardial data, provided estimates a0=0.91±0.11 and μ=5.3±1.2 min. The same method was used in a rat study by Croteau et al (2010 and 2012), and a similar approach, but fitting several ROIs simultaneously, was used by Raylman et al (1994). The model-derived estimates of metabolite fractions were found to be fairly close to the measured fractions (Buck et al., 1991, Fig 5). This function with the reported mean parameter values has been since used for metabolite correction (van den Hoff et al., 1996 and 2001; Wyss et al., 2009). Schiepers et al. (2008) used a modified version of this function, where metabolite fraction starts to increase after a delay time:

, where the delay time τ=0.48 min and λ=0.104. In a dog study, marked blood concentration of [11C]CO2 was seen at ∼4 min (Brown et al., 1988, figure 1). Better approach would be to use the fractions reported by Sun et al (1998), because the values are based on direct blood measurements, independent on the tissue analysis model, and they assessed also other metabolites than [11C]CO2; these values have since been used for metabolite correction in a few studies (Timmer et al., 2011; Wong et al (2013); Hansson et al., 2017; Harms et al., 2018).

[C-11]Acetate parent fractions in blood
Figure 1. [1-11C]acetate fractions in arterial blood (Sun et al., 1998), with fitted Hill function.
Hill function parameters: 0.09957, 2.305, 26.58, 1.0, 0.0.
Parameters are stored in acetate.fit for use with metabcor.

When [1-11C]acetate is used for estimating perfusion, single-tissue compartmental model can be fitted to only the first 3 min of the scan data, obviating the need for metabolite correction (Sciacca RR et al., 2001).

Myocardial [11C]acetate PET

Bolus injection of [1-11C]acetate into coronary artery leads to high extraction and initially monoexponential clearance curve, which very well corresponds to myocardial oxygen consumption. Intravenous administration leads to dispersed input function, containing recirculating [1-11C]acetate and its labelled metabolites. This affects the myocardial TAC shapes, and leads to biased results unless kinetic model with measured input function is used (Buck et al., 1991).

Graphical methods for estimating the release rate of [11C]CO2 are attractive because of their simplicity; also, clearance method is independent on partial volume effect. Clearance of 11C from the myocardium has been found to be bi-exponential (Brown et al., 1987, 1988 and 1989; Armbrecht et al., 1989). The first phase represents the clearance of [11C]CO2. The second compartment is due to the myocardial glutamate pool (Ng et al., 1994). The k1 from bi-exponential and kmono from mono-exponential clearance estimation are correlated to myocardial oxygen consumption (Buxton et al, 1989; Walsh et al., 1989; Armbrecht et al, 1989 and 1990; Ng et al., 1994; Sun et al, 1998; Porenta et al., 1999; Ukkonen et al., 2001; Wong et al., 2013). Parametric kmono and perfusion images can be computed and presented as polar maps (Kotzerke et al., 1990; Miller et al., 1990; Hussain et al., 2009; Croteau et al., 2015). The kmono represents the oxygen consumption in perfused myocardial tissue; in myocardial infarction the regional perfusable tissue fraction (PTF) is reduced, which can be accounted for by multiplying kmono with PTF from myocardial [15O]H2O PET study (Frosfeldt et al., 2005).

Bi- and monoexponential functions that have been used to fit the decreasing myocardial TAC include

, where parameters k1, kc, and kmono correlate with the rate of oxidative metabolism and parameter k2 from compartmental model. Exponential fit is started at the time of onset of the most rapid decline in the TAC (Brown et al., 1988; Buck et al., 1991), not from the peak of the TAC. In practise, the monoexponential fitting of kmono has often been done by fitting line to the linear portion of semilogarithmic plot of the data. In a rat study, semilogarithmic plot was approximately linear 2-20 min p.i. in normoxic and hypoxic hearts and 10-35 min in ischemic hearts (Ng et al., 1994). In human studies, Timmer et al (2011) started the monoexponential fit from the first frame for which four consecutive following frames showed decreasing activity concentrations in the whole myocardium TAC. Hansson et al. (2018) started fit at 6 min, which provided kmono with as good repeatability as k2 from single-tissue compartmental model, although kmono values were lower.

Alternatively, clearance rate constant can be calculated via mean transit time; this method is less sensitive to the shape of the input function and avoids subjective selection of the linear portion of the data used for fitting (Choi et al., 1993).

Several compartmental models have been presented to estimate myocardial oxygen consumption, as reviewed by Klein et al. (2001). One-tissue compartment model analysis of [1-11C]acetate data allows also quantification of myocardial perfusion at rest as well as under stress conditions (van den Hoff et al., 2001; Sciacca RR et al., 2001; Sörensen et al., 2010). This model is a simplification of previous five-compartment model (van den Hoff et al., 1996), and performed best in comparison to three other models in assessment of MBF (Timmer et al., 2010). It was found to provide MBF values in fairly good agreement with actual perfusion values in both healthy individuals and patients with hypertrophic cardiomyopathy over physiological flow ranges under baseline conditions (Sciacca et al., 2001; Timmer et al., 2010), and a reliable index (k2) of oxygen consumption (Timmer et al., 2011; Wong et al., 2013). Arterial blood curve is extracted from left ventricular region, and corrected for metabolism using population-based function. Clustering can be applied in deriving the blood curve from the LV cavity (Harms et al., 2015). Basis function method can be used to calculate parametric images (Harms et al., 2018).

[1-11C]acetate PET has been used to assess myocardial efficiency by measuring both oxygen consumption and stroke volume from the single PET study (Sörensen et al., 2003 and 2010) with good repeatability (Hansson et al., 2018; Wu et al., 2018). Myocardial mass and volume can be calculated by segmenting parametric K1 and VB images (Harms et al., 2016) or from gated uptake images (Hansson et al., 2016).

In atherosclerotic lesions M2 polarized macrophages actively incorporate acetate into lipids, while other cells present in the atheroma, such as vascular smooth muscle cells, contribute minimally to acetate uptake (Fernández-García & Boscá, 2022).

Carimas™ includes the one-exponential fitting (kmono) for assessing myocardial oxygen consumption (Nesterov et al., 2015), and one-tissue compartmental model for the estimation of myocardial perfusion (van den Hoff et al., 2001; TPCMOD0039). Carimas™ user documentation contains further assistance on using the software.

Brain PET with [11C]acetate

Total cerebral oxygen consumption can be measured using inhaled [15O]O2, but astrocytic oxidative metabolism can be measured using [1-11C]acetate (Wyss et al., 2009; Iversen et al., 2014; Arnold et al., 2015). Wyss et al. (2009) used traditional one-tissue compartment model fitting to estimate K1 and k2, with blood volume fraction fixed to 0.05. Metabolite corrected arterial plasma TAC was used as model input: measured blood curves were converted to plasma curves using quadratic polynomial function, which was fitted to plasma/blood-ratios obtained from rat studies (Wyss et al., 2009); metabolite correction was based on previously published metabolite fractions in humans (Buck et al., 1991). K1 was weakly correlated with perfusion (because of low extraction), but k2 seemed to be more correlated with oxygen metabolism than perfusion. This method has been used for example to visualize astrocytic reactivation in MS patients (Kato et al., 2021).

Iversen et al. (2014) developed a three-tissue compartmental model, in which parameter k3 represents the oxidation rate of [11C]acetate. Non-linear mixed effects model was used to fit all study subjects (including three separate groups) simultaneously to avoid overfitting.

In brain glioma patients, SUV ratio using choroid plexus as reference region, and time range 10-30 min after injection, was found to predict overall survival (Kim et al., 2020).

Renal [11C]acetate PET

The perfusion and oxidative metabolism in renal cortex can be assessed with dynamic [1-11C]acetate PET study (Normand et al., 2019). PET images are of good quality even in case of severely reduced renal function, with clear uptake in the renal cortex (Shreve et al., 1995). Acetate is resorbed from glomerular filtrate by active transport in the proximal convoluted tubules (Schafer & Williams, 1985), and therefore the tracer has no observable urinary excretion at least during the first 30 min after tracer administration.

Juillard et al (2007) showed in a pig study that kmono can be calculated from a mono-exponential fit and that it correlates well with renal oxidative metabolism.

When arterial input function is available, one-tissue compartmental model is recommended for the analysis (Shreve et al., 1995; Normand et al., 2019). Both K1 and k2 are reduced in renal disease and renal artery stenosis (Shreve et al., 1995). Shreve et al (1995) corrected the input function for metabolites and included blood volume as one of the model parameter, while Normand et al (2019) do not report doing either of those. Delay time was corrected by Shreve et al (1995) and Normand et al (2019). Shreve et al (1995) reported K1 values in the range 0.65-1.37 mL/(g*min) in healthy subjects, and estimated that the first-pass extraction fraction of acetate in dogs is ∼20-25%. Normand et al (2019) reported K1 values in the range 1.15-2.33, which was 52% of K1 of [15O]H2O; after correction based on the [15O]H2O PET, the [1-11C]acetate PET provided perfusion 3.3±0.5 mL/(g*min) in the renal cortex. The test-retest reproducibility of perfusion in healthy subjects was 0.22 with an ICC of 0.26 (Normand et al., 2019).

[C-11]Acetate parametric images
Figure 2. Renal [1-11C]acetate K1 and k2 images.

Acetate can also be labelled with 13C for studying the oxidative metabolism of kidneys with hyperpolarized MRI (Mikkelsen et al., 2017); diuretic furosemide induced change in oxygen consumption could be demonstrated in rats using both [1-11C]acetate-PET and [1-13C]acetate-MRI.

Liver [11C]acetate PET

In liver studies the portal and arterial contribution to the input function can be taken into account in the analysis method (Chen and Feng, 2004a and 2006; Chen et al. 2004b). For diagnostic purposes, however, SUV or liver-to-blood ratio (LBR) can be sufficient; for instance, both SUVmax and LBR have been shown to detect fatty infiltration in liver steatosis with high accuracy (Nejabat et al., 2018). Sensitivity for detecting hepatic tumours is poor (Roivainen et al., 2013; Li et al., 2016).

Normal SUVs in liver, pancreas, spleen, and adrenal glands have been reported by Malkowski et al (2017).

[11C]Acetate in skeletal muscle

[1-11C]acetate can be used to calculate indices of muscle blood flow and oxidative metabolism, applying two-tissue compartmental model with metabolite corrected plasma input (Croteau et al., 2010; Labbe et al., 2011). The rate constant K1 provides a reliable index of tissue perfusion, because the first-pass extraction fraction of [1-11C]acetate is close to 1 in the resting skeletal muscle. The rapid component of tissue clearance, rate constant k2, is assumed to represent muscle oxidative metabolism (Labbe et al., 2011). Slow [11C]CO2 release from resting muscle may limit the reliability of estimates of oxidative metabolism.

Buchegger et al (2011) have analysed [1-11C]acetate uptake in resting and exercising muscle semi-quantitatively using SUV.

[11C]Acetate in BAT and WAT

Mono-exponential clearance (kmono) has been used as an estimate of oxidative metabolism in human brown adipose tissue (BAT) studies (Ouellet et al., 2012; Blondin et al., 2014, 2015a, 2015b, 2017a, and 2017b). In all of these studies the monoexponential fit was started from the TAC peak. Three-tissue compartmental model with arterial input function and four fitted parameters was found to best describe the kinetics with appropriate stability (Richard et al., 2019).

In white adipose tissue (WAT) [1-11C]acetate uptake is very low (Ouellet et al., 2012) and clearance cannot be measured using exponential fit. Also in BAT in normal conditions the uptake is low and clearance very slow, and estimation of kmono may be prone to errors.

Irreversible two-tissue compartmental model, developed for myocardium (van den Hoff et al., 1996), has been used to estimate both perfusion (K1) and oxidative metabolism (k2) in the BAT of rats (Labbé et al., 2015; Richard et al., 2021) and BAT and WAT of mice (Labbé et al., 2018).

[11C]Acetate in lungs

Early pulmonary retention of [1-11C]acetate has been measured as SUV; lung water content was quantitated from [15O]H2O PET at equilibrium (Sörensen et al., 2006). Monoexponential fit, started 5 min p.i., has been used to calculate kmono (Sörensen et al., 2006).

[11C]Acetate PET in cancer studies

The expression of fatty acid synthase is increased in many tumour types, and cytoplasmic/nuclear acetyl-CoA synthetase is upregulated especially in hypoxic tumours. [1-11C]Acetate PET can be used to assess fatty acid metabolism in tumours, and to study the effects of cancer treatment, such as fatty acid synthase inhibition (Vāvere et al., 2008; Yoshii et al., 2013). In mice prostate cancer xenograft model, radiation therapy led to overexpression of cytoplasmic acetyl-CoA synthetase and increased FDG uptake, but no change in [11C]acetate SUV (Chung et al., 2017).

Urinary excretion of [1-11C]acetate and its labelled metabolites is negligible, and therefore [1-11C]acetate is better for imaging tumours of the bladder and prostate cancer than for example [18F]FDG. In a prospective study, [1-11C]acetate PET/MRI was shown to be feasible for staging of bladder cancer (Salminen et al., 2018).

In head-and-neck tumours, oxidative metabolism has been assessed using kmono, and perfusion as retention index (FUR, tissue activity divided by integral of input curve), or, as relative perfusion index (peak retention in lesion divided by retention in cerebellum) (Sun et al., 2012).

[1-11C]acetate has high uptake in renal parenchyma, and there are conflicting results on whether it is not useful for detecting renal cell carcinoma (Kotzerke et al., 2007; Oyama et al., 2009).

Prostate cancer

Schiepers et al (2008) observed that MTGA for irreversible uptake (Patlak plot) and 2-tissue compartment model, with k4 set to zero, can be used to study the metabolic activity of prostate tumours. Blood metabolites were corrected using previously estimated metabolite function. One-tissue compartment model can be used to calculated parametric images; high K1 (perfusion and extraction) and VT=K1/k2 (anabolic metabolism, carbon retention) is associated with cancer aggressiveness (Regula et al., 2020).

SUV will be sufficient in clinical practice and IMRT treatment planning (Schiepers et al., 2008; Seppälä et al., 2009; Jambor et al., 2012; Leisser et al., 2015; Regula et al., 2020).

See also:


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Updated at: 2023-09-05
Created at: 2008-11-27
Written by: Vesa Oikonen, Chunlei Han